Expectation Maximization (EM) Learning

SamIam's
EM Learning
tool allows the belief engineer to learn the conditional
probabilities in a network based on data, using the
Expectation Maximization (EM) algorithm .
The data must be in the form of a Hugin "case file,"
each line of which represents
one instantiation of the variables in the network.

The first step in using the EM Learning tool is
to design a network structure that includes a node for every variable
mentioned in the case file. Also, you must define state names that
correspond to the state names mentioned in the case file. Generally, you
will initialize the network with uniform probability distributions over
each variable.

Next, open the EM Learning tool by selecting its menu
item from the "Query" menu or clicking the "EM" button on the tool bar. In the
EM Learning dialog window, use the "Browse" button to select the Hugin format
case file you would like to use. You may also choose to modify the default values
for the
convergence threshold
and the maximum number of iterations.
When you have chosen acceptable parameters, click
"OK" to run the EM algorithm. SamIam will make a copy of the open network and
learn the CPT parameters based on the case file you selected.

SamIam 2.2 also provides the function of generating simulated case files.
A simulated case file is a Hugin format case file generated artificially from
a Bayesian network for which the user has already defined conditional probabilities.
To simulate data, select "Generate Simulated Cases"
from the "Tools" menu.
You can choose the number of cases
to write to the file and the
percentage of the data SamIam will destroy at random.